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Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

View ORCID ProfileMichael W. Reimann, Henri Riihimäki, Jason P. Smith, Jānis Lazovskis, Christoph Pokorny, Ran Levi
doi: https://doi.org/10.1101/2020.11.02.363929
Michael W. Reimann
1Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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  • ORCID record for Michael W. Reimann
  • For correspondence: michael.reimann@epfl.ch
Henri Riihimäki
2University of Aberdeen, Aberdeen, UK
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Jason P. Smith
2University of Aberdeen, Aberdeen, UK
3Nottingham Trent University, Nottingham, UK
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Jānis Lazovskis
2University of Aberdeen, Aberdeen, UK
4University of Latvia, Rīga, Latvia
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Christoph Pokorny
1Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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Ran Levi
2University of Aberdeen, Aberdeen, UK
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1 Abstract

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* Co-senior authors

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted November 03, 2020.
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Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
Michael W. Reimann, Henri Riihimäki, Jason P. Smith, Jānis Lazovskis, Christoph Pokorny, Ran Levi
bioRxiv 2020.11.02.363929; doi: https://doi.org/10.1101/2020.11.02.363929
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Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies
Michael W. Reimann, Henri Riihimäki, Jason P. Smith, Jānis Lazovskis, Christoph Pokorny, Ran Levi
bioRxiv 2020.11.02.363929; doi: https://doi.org/10.1101/2020.11.02.363929

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